Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations169
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.3 KiB
Average record size in memory128.8 B

Variable types

Numeric10
Categorical6

Alerts

commodity is highly overall correlated with parent_entity and 4 other fieldsHigh correlation
emissions_category is highly overall correlated with flaring_emissions_MtCO2 and 9 other fieldsHigh correlation
flaring_emissions_MtCO2 is highly overall correlated with emissions_category and 9 other fieldsHigh correlation
fugitive_methane_emissions_MtCH4 is highly overall correlated with emissions_category and 7 other fieldsHigh correlation
fugitive_methane_emissions_MtCO2e is highly overall correlated with emissions_category and 7 other fieldsHigh correlation
own_fuel_use_emissions_MtCO2 is highly overall correlated with production_value and 1 other fieldsHigh correlation
parent_entity is highly overall correlated with commodity and 4 other fieldsHigh correlation
parent_type is highly overall correlated with commodity and 6 other fieldsHigh correlation
product_emissions_MtCO2 is highly overall correlated with emissions_category and 7 other fieldsHigh correlation
production_unit is highly overall correlated with commodity and 5 other fieldsHigh correlation
production_value is highly overall correlated with emissions_category and 9 other fieldsHigh correlation
reporting_entity is highly overall correlated with commodity and 5 other fieldsHigh correlation
total_emissions_MtCO2e is highly overall correlated with emissions_category and 7 other fieldsHigh correlation
total_operational_emissions_MtCO2e is highly overall correlated with emissions_category and 7 other fieldsHigh correlation
venting_emissions_MtCO2 is highly overall correlated with emissions_category and 10 other fieldsHigh correlation
year is highly overall correlated with commodity and 7 other fieldsHigh correlation
year is uniformly distributed Uniform
year has unique values Unique
flaring_emissions_MtCO2 has 47 (27.8%) zeros Zeros
venting_emissions_MtCO2 has 47 (27.8%) zeros Zeros
own_fuel_use_emissions_MtCO2 has 108 (63.9%) zeros Zeros

Reproduction

Analysis started2025-03-29 14:15:48.513669
Analysis finished2025-03-29 14:16:05.719675
Duration17.21 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct169
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1938
Minimum1854
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:05.885522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1854
5-th percentile1862.4
Q11896
median1938
Q31980
95-th percentile2013.6
Maximum2022
Range168
Interquartile range (IQR)84

Descriptive statistics

Standard deviation48.930222
Coefficient of variation (CV)0.025247793
Kurtosis-1.2
Mean1938
Median Absolute Deviation (MAD)42
Skewness0
Sum327522
Variance2394.1667
MonotonicityStrictly increasing
2025-03-29T11:16:06.098269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1854 1
 
0.6%
1970 1
 
0.6%
1962 1
 
0.6%
1963 1
 
0.6%
1964 1
 
0.6%
1965 1
 
0.6%
1966 1
 
0.6%
1967 1
 
0.6%
1968 1
 
0.6%
1969 1
 
0.6%
Other values (159) 159
94.1%
ValueCountFrequency (%)
1854 1
0.6%
1855 1
0.6%
1856 1
0.6%
1857 1
0.6%
1858 1
0.6%
1859 1
0.6%
1860 1
0.6%
1861 1
0.6%
1862 1
0.6%
1863 1
0.6%
ValueCountFrequency (%)
2022 1
0.6%
2021 1
0.6%
2020 1
0.6%
2019 1
0.6%
2018 1
0.6%
2017 1
0.6%
2016 1
0.6%
2015 1
0.6%
2014 1
0.6%
2013 1
0.6%

parent_entity
Categorical

High correlation 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Abu Dhabi National Oil Company
122 
Adaro Energy
36 
Adani Enterprises
 
11

Length

Max length30
Median length30
Mean length25.319527
Min length12

Characters and Unicode

Total characters4279
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdaro Energy
2nd rowAdaro Energy
3rd rowAdaro Energy
4th rowAdaro Energy
5th rowAdaro Energy

Common Values

ValueCountFrequency (%)
Abu Dhabi National Oil Company 122
72.2%
Adaro Energy 36
 
21.3%
Adani Enterprises 11
 
6.5%

Length

2025-03-29T11:16:06.310401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:06.494110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
abu 122
17.3%
dhabi 122
17.3%
national 122
17.3%
oil 122
17.3%
company 122
17.3%
adaro 36
 
5.1%
energy 36
 
5.1%
adani 11
 
1.6%
enterprises 11
 
1.6%

Most occurring characters

ValueCountFrequency (%)
535
12.5%
a 535
12.5%
i 388
 
9.1%
n 302
 
7.1%
o 280
 
6.5%
l 244
 
5.7%
b 244
 
5.7%
A 169
 
3.9%
y 158
 
3.7%
t 133
 
3.1%
Other values (14) 1291
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
535
12.5%
a 535
12.5%
i 388
 
9.1%
n 302
 
7.1%
o 280
 
6.5%
l 244
 
5.7%
b 244
 
5.7%
A 169
 
3.9%
y 158
 
3.7%
t 133
 
3.1%
Other values (14) 1291
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
535
12.5%
a 535
12.5%
i 388
 
9.1%
n 302
 
7.1%
o 280
 
6.5%
l 244
 
5.7%
b 244
 
5.7%
A 169
 
3.9%
y 158
 
3.7%
t 133
 
3.1%
Other values (14) 1291
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
535
12.5%
a 535
12.5%
i 388
 
9.1%
n 302
 
7.1%
o 280
 
6.5%
l 244
 
5.7%
b 244
 
5.7%
A 169
 
3.9%
y 158
 
3.7%
t 133
 
3.1%
Other values (14) 1291
30.2%

parent_type
Categorical

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
State-owned Entity
122 
Investor-owned Company
47 

Length

Max length22
Median length18
Mean length19.112426
Min length18

Characters and Unicode

Total characters3230
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInvestor-owned Company
2nd rowInvestor-owned Company
3rd rowInvestor-owned Company
4th rowInvestor-owned Company
5th rowInvestor-owned Company

Common Values

ValueCountFrequency (%)
State-owned Entity 122
72.2%
Investor-owned Company 47
 
27.8%

Length

2025-03-29T11:16:06.680341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:06.836621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
state-owned 122
36.1%
entity 122
36.1%
investor-owned 47
 
13.9%
company 47
 
13.9%

Most occurring characters

ValueCountFrequency (%)
t 535
16.6%
n 385
11.9%
e 338
10.5%
o 263
 
8.1%
y 169
 
5.2%
a 169
 
5.2%
- 169
 
5.2%
w 169
 
5.2%
d 169
 
5.2%
169
 
5.2%
Other values (10) 695
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 535
16.6%
n 385
11.9%
e 338
10.5%
o 263
 
8.1%
y 169
 
5.2%
a 169
 
5.2%
- 169
 
5.2%
w 169
 
5.2%
d 169
 
5.2%
169
 
5.2%
Other values (10) 695
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 535
16.6%
n 385
11.9%
e 338
10.5%
o 263
 
8.1%
y 169
 
5.2%
a 169
 
5.2%
- 169
 
5.2%
w 169
 
5.2%
d 169
 
5.2%
169
 
5.2%
Other values (10) 695
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 535
16.6%
n 385
11.9%
e 338
10.5%
o 263
 
8.1%
y 169
 
5.2%
a 169
 
5.2%
- 169
 
5.2%
w 169
 
5.2%
d 169
 
5.2%
169
 
5.2%
Other values (10) 695
21.5%

reporting_entity
Categorical

High correlation 

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
ADGAS
50 
Abu Dhabi National Oil Company
47 
Adaro Energy
36 
Abu Dhabi
25 
Adani Enterprises
11 

Length

Max length30
Median length17
Mean length14.816568
Min length5

Characters and Unicode

Total characters2504
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdaro Energy
2nd rowAdaro Energy
3rd rowAdaro Energy
4th rowAdaro Energy
5th rowAdaro Energy

Common Values

ValueCountFrequency (%)
ADGAS 50
29.6%
Abu Dhabi National Oil Company 47
27.8%
Adaro Energy 36
21.3%
Abu Dhabi 25
14.8%
Adani Enterprises 11
 
6.5%

Length

2025-03-29T11:16:07.096739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:07.269980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
abu 72
16.8%
dhabi 72
16.8%
adgas 50
11.7%
national 47
11.0%
oil 47
11.0%
company 47
11.0%
adaro 36
8.4%
energy 36
8.4%
adani 11
 
2.6%
enterprises 11
 
2.6%

Most occurring characters

ValueCountFrequency (%)
260
 
10.4%
a 260
 
10.4%
A 219
 
8.7%
i 188
 
7.5%
n 152
 
6.1%
b 144
 
5.8%
o 130
 
5.2%
D 122
 
4.9%
l 94
 
3.8%
r 94
 
3.8%
Other values (16) 841
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
260
 
10.4%
a 260
 
10.4%
A 219
 
8.7%
i 188
 
7.5%
n 152
 
6.1%
b 144
 
5.8%
o 130
 
5.2%
D 122
 
4.9%
l 94
 
3.8%
r 94
 
3.8%
Other values (16) 841
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
260
 
10.4%
a 260
 
10.4%
A 219
 
8.7%
i 188
 
7.5%
n 152
 
6.1%
b 144
 
5.8%
o 130
 
5.2%
D 122
 
4.9%
l 94
 
3.8%
r 94
 
3.8%
Other values (16) 841
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
260
 
10.4%
a 260
 
10.4%
A 219
 
8.7%
i 188
 
7.5%
n 152
 
6.1%
b 144
 
5.8%
o 130
 
5.2%
D 122
 
4.9%
l 94
 
3.8%
r 94
 
3.8%
Other values (16) 841
33.6%

commodity
Categorical

High correlation 

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Natural Gas
61 
Oil & NGL
61 
Sub-Bituminous Coal
41 
Metallurgical Coal
 
6

Length

Max length19
Median length18
Mean length12.467456
Min length9

Characters and Unicode

Total characters2107
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSub-Bituminous Coal
2nd rowMetallurgical Coal
3rd rowSub-Bituminous Coal
4th rowMetallurgical Coal
5th rowSub-Bituminous Coal

Common Values

ValueCountFrequency (%)
Natural Gas 61
36.1%
Oil & NGL 61
36.1%
Sub-Bituminous Coal 41
24.3%
Metallurgical Coal 6
 
3.6%

Length

2025-03-29T11:16:07.457369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:07.616714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
natural 61
15.3%
gas 61
15.3%
oil 61
15.3%
61
15.3%
ngl 61
15.3%
coal 47
11.8%
sub-bituminous 41
10.3%
metallurgical 6
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 242
11.5%
230
10.9%
u 190
 
9.0%
l 187
 
8.9%
i 149
 
7.1%
N 122
 
5.8%
G 122
 
5.8%
t 108
 
5.1%
s 102
 
4.8%
o 88
 
4.2%
Other values (15) 567
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 242
11.5%
230
10.9%
u 190
 
9.0%
l 187
 
8.9%
i 149
 
7.1%
N 122
 
5.8%
G 122
 
5.8%
t 108
 
5.1%
s 102
 
4.8%
o 88
 
4.2%
Other values (15) 567
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 242
11.5%
230
10.9%
u 190
 
9.0%
l 187
 
8.9%
i 149
 
7.1%
N 122
 
5.8%
G 122
 
5.8%
t 108
 
5.1%
s 102
 
4.8%
o 88
 
4.2%
Other values (15) 567
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 242
11.5%
230
10.9%
u 190
 
9.0%
l 187
 
8.9%
i 149
 
7.1%
N 122
 
5.8%
G 122
 
5.8%
t 108
 
5.1%
s 102
 
4.8%
o 88
 
4.2%
Other values (15) 567
26.9%

production_value
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.61064
Minimum0.22
Maximum2689.4303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:07.814543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile3.30748
Q126.78
median294.1827
Q3779.3
95-th percentile1949.1417
Maximum2689.4303
Range2689.2103
Interquartile range (IQR)752.52

Descriptive statistics

Standard deviation638.2287
Coefficient of variation (CV)1.2330286
Kurtosis2.2171868
Mean517.61064
Median Absolute Deviation (MAD)283.8227
Skewness1.5674625
Sum87476.198
Variance407335.87
MonotonicityNot monotonic
2025-03-29T11:16:08.030401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1277.5 4
 
2.4%
2689.4303 3
 
1.8%
29.8607 2
 
1.2%
97.3236 2
 
1.2%
25.7325 1
 
0.6%
10.95 1
 
0.6%
13.505 1
 
0.6%
14.6 1
 
0.6%
18.25 1
 
0.6%
22.265 1
 
0.6%
Other values (152) 152
89.9%
ValueCountFrequency (%)
0.22 1
0.6%
0.9 1
0.6%
0.9125 1
0.6%
1 1
0.6%
1.4 1
0.6%
1.825 1
0.6%
1.8432 1
0.6%
2.4 1
0.6%
2.8458 1
0.6%
4 1
0.6%
ValueCountFrequency (%)
2689.4303 3
1.8%
2612.5894 1
 
0.6%
2510.1349 1
 
0.6%
2129.6916 1
 
0.6%
1965.8692 1
 
0.6%
1964.3206 1
 
0.6%
1959.1368 1
 
0.6%
1934.1491 1
 
0.6%
1918.428 1
 
0.6%
1618.0778 1
 
0.6%

production_unit
Categorical

High correlation 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Bcf/yr
61 
Million bbl/yr
61 
Million tonnes/yr
47 

Length

Max length17
Median length14
Mean length11.946746
Min length6

Characters and Unicode

Total characters2019
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMillion tonnes/yr
2nd rowMillion tonnes/yr
3rd rowMillion tonnes/yr
4th rowMillion tonnes/yr
5th rowMillion tonnes/yr

Common Values

ValueCountFrequency (%)
Bcf/yr 61
36.1%
Million bbl/yr 61
36.1%
Million tonnes/yr 47
27.8%

Length

2025-03-29T11:16:08.231189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:08.515906image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
million 108
39.0%
bcf/yr 61
22.0%
bbl/yr 61
22.0%
tonnes/yr 47
17.0%

Most occurring characters

ValueCountFrequency (%)
l 277
13.7%
i 216
10.7%
n 202
10.0%
/ 169
8.4%
y 169
8.4%
r 169
8.4%
o 155
7.7%
b 122
 
6.0%
M 108
 
5.3%
108
 
5.3%
Other values (6) 324
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 277
13.7%
i 216
10.7%
n 202
10.0%
/ 169
8.4%
y 169
8.4%
r 169
8.4%
o 155
7.7%
b 122
 
6.0%
M 108
 
5.3%
108
 
5.3%
Other values (6) 324
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 277
13.7%
i 216
10.7%
n 202
10.0%
/ 169
8.4%
y 169
8.4%
r 169
8.4%
o 155
7.7%
b 122
 
6.0%
M 108
 
5.3%
108
 
5.3%
Other values (6) 324
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 277
13.7%
i 216
10.7%
n 202
10.0%
/ 169
8.4%
y 169
8.4%
r 169
8.4%
o 155
7.7%
b 122
 
6.0%
M 108
 
5.3%
108
 
5.3%
Other values (6) 324
16.0%

product_emissions_MtCO2
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.31173
Minimum0.0985
Maximum501.613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:08.696496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0985
5-th percentile1.683
Q115.3799
median63.615
Q3129.0338
95-th percentile371.45684
Maximum501.613
Range501.5145
Interquartile range (IQR)113.6539

Descriptive statistics

Standard deviation122.62685
Coefficient of variation (CV)1.1869596
Kurtosis1.6371562
Mean103.31173
Median Absolute Deviation (MAD)50.0309
Skewness1.55823
Sum17459.683
Variance15037.344
MonotonicityNot monotonic
2025-03-29T11:16:08.911729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
474.4988 4
 
2.4%
143.7063 3
 
1.8%
1.5956 2
 
1.2%
5.2004 2
 
1.2%
9.5578 1
 
0.6%
4.0671 1
 
0.6%
5.0161 1
 
0.6%
5.4228 1
 
0.6%
6.7786 1
 
0.6%
8.2698 1
 
0.6%
Other values (152) 152
89.9%
ValueCountFrequency (%)
0.0985 1
0.6%
0.2364 1
0.6%
0.3389 1
0.6%
0.5864 1
0.6%
0.6779 1
0.6%
0.9258 1
0.6%
1.3395 1
0.6%
1.5956 2
1.2%
1.8141 1
0.6%
2.088 1
0.6%
ValueCountFrequency (%)
501.613 1
 
0.6%
474.4988 4
2.4%
440.606 1
 
0.6%
406.7132 1
 
0.6%
396.8983 1
 
0.6%
372.237 1
 
0.6%
370.2866 1
 
0.6%
352.1877 1
 
0.6%
339.1844 1
 
0.6%
332.1677 1
 
0.6%

flaring_emissions_MtCO2
Real number (ℝ)

High correlation  Zeros 

Distinct116
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2209982
Minimum0
Maximum7.998
Zeros47
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:09.134630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0646
Q31.7422
95-th percentile5.92276
Maximum7.998
Range7.998
Interquartile range (IQR)1.7422

Descriptive statistics

Standard deviation2.1162095
Coefficient of variation (CV)1.7331799
Kurtosis1.6375796
Mean1.2209982
Median Absolute Deviation (MAD)0.0646
Skewness1.6836119
Sum206.3487
Variance4.4783427
MonotonicityNot monotonic
2025-03-29T11:16:09.352226image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
27.8%
7.5657 4
 
2.4%
0.2494 3
 
1.8%
0.0028 2
 
1.2%
0.009 2
 
1.2%
2.3417 1
 
0.6%
1.0635 1
 
0.6%
1.0298 1
 
0.6%
1.6857 1
 
0.6%
2.2422 1
 
0.6%
Other values (106) 106
62.7%
ValueCountFrequency (%)
0 47
27.8%
0.0002 1
 
0.6%
0.0004 1
 
0.6%
0.0016 1
 
0.6%
0.0023 1
 
0.6%
0.0028 2
 
1.2%
0.0036 1
 
0.6%
0.0044 1
 
0.6%
0.005 1
 
0.6%
0.0054 1
 
0.6%
ValueCountFrequency (%)
7.998 1
 
0.6%
7.5657 4
2.4%
7.0253 1
 
0.6%
6.4849 1
 
0.6%
6.3284 1
 
0.6%
5.9352 1
 
0.6%
5.9041 1
 
0.6%
5.6155 1
 
0.6%
5.4082 1
 
0.6%
5.2963 1
 
0.6%

venting_emissions_MtCO2
Real number (ℝ)

High correlation  Zeros 

Distinct116
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75883609
Minimum0
Maximum4.1005
Zeros47
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:09.568643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.4188
Q31.1094
95-th percentile2.97176
Maximum4.1005
Range4.1005
Interquartile range (IQR)1.1094

Descriptive statistics

Standard deviation0.97097408
Coefficient of variation (CV)1.2795571
Kurtosis2.4504728
Mean0.75883609
Median Absolute Deviation (MAD)0.4188
Skewness1.6246269
Sum128.2433
Variance0.94279065
MonotonicityNot monotonic
2025-03-29T11:16:09.765822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
27.8%
1.8186 4
 
2.4%
4.1005 3
 
1.8%
0.0455 2
 
1.2%
0.1484 2
 
1.2%
0.5629 1
 
0.6%
0.2556 1
 
0.6%
0.2475 1
 
0.6%
0.4052 1
 
0.6%
0.5389 1
 
0.6%
Other values (106) 106
62.7%
ValueCountFrequency (%)
0 47
27.8%
0.0013 1
 
0.6%
0.0026 1
 
0.6%
0.0028 1
 
0.6%
0.0067 1
 
0.6%
0.0104 1
 
0.6%
0.0156 1
 
0.6%
0.0192 1
 
0.6%
0.0208 1
 
0.6%
0.026 1
 
0.6%
ValueCountFrequency (%)
4.1005 3
1.8%
3.9833 1
 
0.6%
3.8271 1
 
0.6%
3.2471 1
 
0.6%
2.9973 1
 
0.6%
2.9949 1
 
0.6%
2.987 1
 
0.6%
2.9489 1
 
0.6%
2.925 1
 
0.6%
2.467 1
 
0.6%

own_fuel_use_emissions_MtCO2
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9477568
Minimum0
Maximum8.2293
Zeros108
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:09.966561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.7779
95-th percentile5.9641
Maximum8.2293
Range8.2293
Interquartile range (IQR)0.7779

Descriptive statistics

Standard deviation1.9587141
Coefficient of variation (CV)2.0666843
Kurtosis4.6297705
Mean0.9477568
Median Absolute Deviation (MAD)0
Skewness2.3161049
Sum160.1709
Variance3.8365608
MonotonicityNot monotonic
2025-03-29T11:16:10.319388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108
63.9%
8.2293 3
 
1.8%
0.2978 2
 
1.2%
0.0914 2
 
1.2%
2.1313 1
 
0.6%
1.0871 1
 
0.6%
1.1798 1
 
0.6%
1.2157 1
 
0.6%
1.2506 1
 
0.6%
1.2844 1
 
0.6%
Other values (48) 48
28.4%
ValueCountFrequency (%)
0 108
63.9%
0.0056 1
 
0.6%
0.0135 1
 
0.6%
0.053 1
 
0.6%
0.0767 1
 
0.6%
0.0914 2
 
1.2%
0.1196 1
 
0.6%
0.1444 1
 
0.6%
0.1647 1
 
0.6%
0.2233 1
 
0.6%
ValueCountFrequency (%)
8.2293 3
1.8%
7.9941 1
 
0.6%
7.6806 1
 
0.6%
6.5165 1
 
0.6%
6.0153 1
 
0.6%
6.0105 1
 
0.6%
5.9947 1
 
0.6%
5.9182 1
 
0.6%
5.8701 1
 
0.6%
4.9511 1
 
0.6%

fugitive_methane_emissions_MtCO2e
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9590314
Minimum0.0183
Maximum39.7481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:10.512113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0183
5-th percentile0.26174
Q12.1448
median7.2107
Q314.9693
95-th percentile28.80708
Maximum39.7481
Range39.7298
Interquartile range (IQR)12.8245

Descriptive statistics

Standard deviation9.4649442
Coefficient of variation (CV)0.95038803
Kurtosis1.0523957
Mean9.9590314
Median Absolute Deviation (MAD)6.0338
Skewness1.1942734
Sum1683.0763
Variance89.585168
MonotonicityNot monotonic
2025-03-29T11:16:10.728653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.5557 4
 
2.4%
39.7481 3
 
1.8%
0.4413 2
 
1.2%
1.4384 2
 
1.2%
0.5148 1
 
0.6%
0.219 1
 
0.6%
0.2702 1
 
0.6%
0.2921 1
 
0.6%
0.3651 1
 
0.6%
0.4454 1
 
0.6%
Other values (152) 152
89.9%
ValueCountFrequency (%)
0.0183 1
0.6%
0.0272 1
0.6%
0.0365 1
0.6%
0.0654 1
0.6%
0.0662 1
0.6%
0.146 1
0.6%
0.2049 1
0.6%
0.219 1
0.6%
0.2561 1
0.6%
0.2702 1
0.6%
ValueCountFrequency (%)
39.7481 3
1.8%
38.6125 1
 
0.6%
37.0983 1
 
0.6%
31.4755 1
 
0.6%
29.0543 1
 
0.6%
29.0315 1
 
0.6%
28.9548 1
 
0.6%
28.5855 1
 
0.6%
28.3532 1
 
0.6%
27.016 1
 
0.6%

fugitive_methane_emissions_MtCH4
Real number (ℝ)

High correlation 

Distinct161
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35567751
Minimum0.0007
Maximum1.4196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:10.935419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0007
5-th percentile0.0093
Q10.0766
median0.2575
Q30.5346
95-th percentile1.02882
Maximum1.4196
Range1.4189
Interquartile range (IQR)0.458

Descriptive statistics

Standard deviation0.33803336
Coefficient of variation (CV)0.95039284
Kurtosis1.0524943
Mean0.35567751
Median Absolute Deviation (MAD)0.2155
Skewness1.1942909
Sum60.1095
Variance0.11426656
MonotonicityNot monotonic
2025-03-29T11:16:11.149770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9127 4
 
2.4%
1.4196 3
 
1.8%
0.3834 2
 
1.2%
0.0158 2
 
1.2%
0.0514 2
 
1.2%
0.0078 1
 
0.6%
0.0096 1
 
0.6%
0.0104 1
 
0.6%
0.013 1
 
0.6%
0.0159 1
 
0.6%
Other values (151) 151
89.3%
ValueCountFrequency (%)
0.0007 1
0.6%
0.001 1
0.6%
0.0013 1
0.6%
0.0023 1
0.6%
0.0024 1
0.6%
0.0052 1
0.6%
0.0073 1
0.6%
0.0078 1
0.6%
0.0091 1
0.6%
0.0096 1
0.6%
ValueCountFrequency (%)
1.4196 3
1.8%
1.379 1
 
0.6%
1.3249 1
 
0.6%
1.1241 1
 
0.6%
1.0377 1
 
0.6%
1.0368 1
 
0.6%
1.0341 1
 
0.6%
1.0209 1
 
0.6%
1.0126 1
 
0.6%
0.9649 1
 
0.6%

total_operational_emissions_MtCO2e
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.886622
Minimum0.025
Maximum52.3273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:11.349073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.29194
Q12.2339
median8.6629
Q319.9125
95-th percentile37.92372
Maximum52.3273
Range52.3023
Interquartile range (IQR)17.6786

Descriptive statistics

Standard deviation12.752361
Coefficient of variation (CV)0.98958141
Kurtosis0.88205565
Mean12.886622
Median Absolute Deviation (MAD)7.6247
Skewness1.1967512
Sum2177.8391
Variance162.62272
MonotonicityNot monotonic
2025-03-29T11:16:11.574159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.94 4
 
2.4%
52.3273 3
 
1.8%
0.581 2
 
1.2%
1.8936 2
 
1.2%
0.7038 1
 
0.6%
0.2995 1
 
0.6%
0.3694 1
 
0.6%
0.3993 1
 
0.6%
0.4991 1
 
0.6%
0.609 1
 
0.6%
Other values (152) 152
89.9%
ValueCountFrequency (%)
0.025 1
0.6%
0.0359 1
0.6%
0.0499 1
0.6%
0.0662 1
0.6%
0.0861 1
0.6%
0.1997 1
0.6%
0.2049 1
0.6%
0.271 1
0.6%
0.2869 1
0.6%
0.2995 1
0.6%
ValueCountFrequency (%)
52.3273 3
1.8%
50.8322 1
 
0.6%
48.8388 1
 
0.6%
41.4366 1
 
0.6%
38.2492 1
 
0.6%
38.2191 1
 
0.6%
38.1182 1
 
0.6%
37.632 1
 
0.6%
37.3262 1
 
0.6%
36.9366 1
 
0.6%

total_emissions_MtCO2e
Real number (ℝ)

High correlation 

Distinct162
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.19835
Minimum0.1344
Maximum538.5495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2025-03-29T11:16:11.776989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.1344
5-th percentile2.1766
Q117.3642
median74.0578
Q3144.2469
95-th percentile398.80924
Maximum538.5495
Range538.4151
Interquartile range (IQR)126.8827

Descriptive statistics

Standard deviation131.23439
Coefficient of variation (CV)1.1293998
Kurtosis1.4409472
Mean116.19835
Median Absolute Deviation (MAD)60.1381
Skewness1.4697882
Sum19637.521
Variance17222.466
MonotonicityNot monotonic
2025-03-29T11:16:12.008714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
509.4388 4
 
2.4%
196.0336 3
 
1.8%
2.1766 2
 
1.2%
7.094 2
 
1.2%
10.2616 1
 
0.6%
4.3666 1
 
0.6%
5.3855 1
 
0.6%
5.8222 1
 
0.6%
7.2777 1
 
0.6%
8.8788 1
 
0.6%
Other values (152) 152
89.9%
ValueCountFrequency (%)
0.1344 1
0.6%
0.3225 1
0.6%
0.3639 1
0.6%
0.6527 1
0.6%
0.7278 1
0.6%
1.2629 1
0.6%
1.8272 1
0.6%
2.0191 1
0.6%
2.1766 2
1.2%
2.67 1
0.6%
ValueCountFrequency (%)
538.5495 1
 
0.6%
509.4388 4
2.4%
473.0503 1
 
0.6%
436.6618 1
 
0.6%
426.1241 1
 
0.6%
399.6468 1
 
0.6%
397.5529 1
 
0.6%
378.1212 1
 
0.6%
364.1604 1
 
0.6%
356.6271 1
 
0.6%

emissions_category
Categorical

High correlation 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
High
95 
Low
74 

Length

Max length4
Median length4
Mean length3.5621302
Min length3

Characters and Unicode

Total characters602
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowLow
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High 95
56.2%
Low 74
43.8%

Length

2025-03-29T11:16:12.203454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T11:16:12.347780image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
high 95
56.2%
low 74
43.8%

Most occurring characters

ValueCountFrequency (%)
H 95
15.8%
i 95
15.8%
g 95
15.8%
h 95
15.8%
L 74
12.3%
o 74
12.3%
w 74
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 95
15.8%
i 95
15.8%
g 95
15.8%
h 95
15.8%
L 74
12.3%
o 74
12.3%
w 74
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 95
15.8%
i 95
15.8%
g 95
15.8%
h 95
15.8%
L 74
12.3%
o 74
12.3%
w 74
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 95
15.8%
i 95
15.8%
g 95
15.8%
h 95
15.8%
L 74
12.3%
o 74
12.3%
w 74
12.3%

Interactions

2025-03-29T11:16:03.556762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:49.529275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:51.823669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.337910image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.703517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.207766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.546421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.226835image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.706159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.177071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.701095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:49.703810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:51.991946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.479505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.879685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.346736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.691094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.375540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.872494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.322358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.827697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:49.952421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.103604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.610080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.028697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.474905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.807905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.529050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.021600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.451385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.964832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:50.113476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.295659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.732806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.177999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.602597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.029319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.660537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.151212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.587330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.110555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:50.324862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.431555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.884234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.338957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.745003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.227167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.836327image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.314615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.731823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.240700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:50.583593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.589086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.012232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.473169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.862612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.346065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.963072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.444462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.870686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.358085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:50.822662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.768964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.138050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.618607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.999720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.594902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.122722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.585246image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.998810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.514296image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:51.068323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:52.911364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.286441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.761288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.143050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.741892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.275791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.739880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.145973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.664722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:51.310837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.073275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.432775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:55.927983image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.285625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:58.940765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.429745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:01.895745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.286728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:04.797954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:51.639935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:53.197482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:54.574388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:56.069182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:57.427040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:15:59.089227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:00.570385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:02.045262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-29T11:16:03.428663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-29T11:16:12.484301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
commodityemissions_categoryflaring_emissions_MtCO2fugitive_methane_emissions_MtCH4fugitive_methane_emissions_MtCO2eown_fuel_use_emissions_MtCO2parent_entityparent_typeproduct_emissions_MtCO2production_unitproduction_valuereporting_entitytotal_emissions_MtCO2etotal_operational_emissions_MtCO2eventing_emissions_MtCO2year
commodity1.0000.3800.4370.3430.3430.3880.7150.9940.3720.9970.3790.7590.3830.3080.3840.705
emissions_category0.3801.0000.5160.7830.7830.3990.2110.1680.9160.3510.5680.5580.9390.7990.5610.693
flaring_emissions_MtCO20.4370.5161.0000.5980.5980.0240.1590.3230.7120.5580.7540.4670.7230.6430.8120.768
fugitive_methane_emissions_MtCH40.3430.7830.5981.0001.0000.2870.3220.4290.8650.4130.9050.4200.8930.9960.7750.283
fugitive_methane_emissions_MtCO2e0.3430.7830.5981.0001.0000.2870.3220.4290.8650.4130.9050.4200.8930.9960.7750.283
own_fuel_use_emissions_MtCO20.3880.3990.0240.2870.2871.0000.1280.283-0.1720.4980.5070.359-0.1160.3130.556-0.047
parent_entity0.7150.2110.1590.3220.3220.1281.0000.9970.2320.7030.3920.9940.2200.2650.3860.771
parent_type0.9940.1680.3230.4290.4290.2830.9971.0000.3480.9970.5950.9910.3590.3980.5870.843
product_emissions_MtCO20.3720.9160.7120.8650.865-0.1720.2320.3481.0000.4640.7260.4470.9970.8640.5840.442
production_unit0.9970.3510.5580.4130.4130.4980.7030.9970.4641.0000.4880.9210.4760.3810.4940.798
production_value0.3790.5680.7540.9050.9050.5070.3920.5950.7260.4881.0000.4300.7640.9310.9510.458
reporting_entity0.7590.5580.4670.4200.4200.3590.9940.9910.4470.9210.4301.0000.4530.4010.4350.810
total_emissions_MtCO2e0.3830.9390.7230.8930.893-0.1160.2200.3590.9970.4760.7640.4531.0000.8930.6280.445
total_operational_emissions_MtCO2e0.3080.7990.6430.9960.9960.3130.2650.3980.8640.3810.9310.4010.8931.0000.8140.330
venting_emissions_MtCO20.3840.5610.8120.7750.7750.5560.3860.5870.5840.4940.9510.4350.6280.8141.0000.527
year0.7050.6930.7680.2830.283-0.0470.7710.8430.4420.7980.4580.8100.4450.3300.5271.000

Missing values

2025-03-29T11:16:05.134746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-29T11:16:05.514636image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearparent_entityparent_typereporting_entitycommodityproduction_valueproduction_unitproduct_emissions_MtCO2flaring_emissions_MtCO2venting_emissions_MtCO2own_fuel_use_emissions_MtCO2fugitive_methane_emissions_MtCO2efugitive_methane_emissions_MtCH4total_operational_emissions_MtCO2etotal_emissions_MtCO2eemissions_category
01854Adaro EnergyInvestor-owned CompanyAdaro EnergySub-Bituminous Coal50.9000Million tonnes/yr92.34010.00.00.010.43160.372610.4316102.7717High
11855Adaro EnergyInvestor-owned CompanyAdaro EnergyMetallurgical Coal0.9000Million tonnes/yr2.39890.00.00.00.27100.00970.27102.6700Low
21856Adaro EnergyInvestor-owned CompanyAdaro EnergySub-Bituminous Coal48.2300Million tonnes/yr87.49630.00.00.09.88440.35309.884497.3807High
31857Adaro EnergyInvestor-owned CompanyAdaro EnergyMetallurgical Coal5.7700Million tonnes/yr15.37990.00.00.01.73750.06211.737517.1174Low
41858Adaro EnergyInvestor-owned CompanyAdaro EnergySub-Bituminous Coal52.8073Million tonnes/yr95.80020.00.00.010.82250.386510.8225106.6227High
51859Adaro EnergyInvestor-owned CompanyAdaro EnergyMetallurgical Coal5.2227Million tonnes/yr13.92110.00.00.01.57270.05621.572715.4938Low
61860Adaro EnergyInvestor-owned CompanyAdaro EnergySub-Bituminous Coal49.6223Million tonnes/yr90.02220.00.00.010.16970.363210.1697100.1919High
71861Adaro EnergyInvestor-owned CompanyAdaro EnergyMetallurgical Coal4.9077Million tonnes/yr13.08150.00.00.01.47780.05281.477814.5593Low
81862Adaro EnergyInvestor-owned CompanyAdaro EnergySub-Bituminous Coal49.8542Million tonnes/yr90.44290.00.00.010.21730.364910.2173100.6601High
91863Adaro EnergyInvestor-owned CompanyAdaro EnergyMetallurgical Coal2.8458Million tonnes/yr7.58550.00.00.00.85690.03060.85698.4424Low
yearparent_entityparent_typereporting_entitycommodityproduction_valueproduction_unitproduct_emissions_MtCO2flaring_emissions_MtCO2venting_emissions_MtCO2own_fuel_use_emissions_MtCO2fugitive_methane_emissions_MtCO2efugitive_methane_emissions_MtCH4total_operational_emissions_MtCO2etotal_emissions_MtCO2eemissions_category
1592013Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyNatural Gas1959.1368Bcf/yr104.68400.18172.98705.994728.95481.034138.1182142.8022High
1602014Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyOil & NGL996.9280Million bbl/yr370.28665.90411.41920.000019.94300.712327.2663397.5529High
1612015Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyNatural Gas1934.1491Bcf/yr103.34880.17942.94895.918228.58551.020937.6320140.9809High
1622016Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyOil & NGL1002.1790Million bbl/yr372.23705.93521.42660.000020.04810.716027.4099399.6468High
1632017Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyNatural Gas1964.3206Bcf/yr104.96100.18222.99496.010529.03151.036838.2191143.1801High
1642018Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyOil & NGL1068.5750Million bbl/yr396.89836.32841.52110.000021.37630.763429.2258426.1241High
1652019Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyNatural Gas1918.4280Bcf/yr102.50880.17792.92505.870128.35321.012637.3262139.8350High
1662020Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyOil & NGL1095.0000Million bbl/yr406.71326.48491.55880.000021.90490.782329.9486436.6618High
1672021Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyNatural Gas1965.8692Bcf/yr105.04380.18232.99736.015329.05431.037738.2492143.2930High
1682022Abu Dhabi National Oil CompanyState-owned EntityAbu Dhabi National Oil CompanyOil & NGL1186.2500Million bbl/yr440.60607.02531.68870.000023.73030.847532.4443473.0503High